Echo-Teddy: Preliminary Design and Development of Large Language Model-based Social Robot for Autistic Students

2025-02-06

AI Summary

Autistic students require tailored interventions to develop social-communication skills, yet existing robotic solutions lack deep integration of large language models (LLMs) and fail to address ethical, developmental, and pedagogical constraints in special education contexts. Method: We designed and implemented Echo-Teddy—a physical, LLM-driven social robot built on Raspberry Pi, featuring custom speech synthesis/recognition and basic locomotion control. Guided by three principles—customizability, ethics-by-design, and age-appropriateness—we employed mixed methods: iterative prototyping, expert interviews with special educators, and reflective practice analysis. Contribution/Results: We deliver a functional prototype, empirically identify key classroom deployment challenges (e.g., UI accessibility, curricular alignment), and introduce the first reusable design framework—with empirical validation—for AI-powered social robots targeting autistic children. This work advances embodied, ethically grounded, and educationally integrated LLM applications in special needs settings.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Autistic students often face challenges in social interaction, which can hinder their educational and personal development. This study introduces Echo-Teddy, a Large Language Model (LLM)-based social robot designed to support autistic students in developing social and communication skills. Unlike previous chatbot-based solutions, Echo-Teddy leverages advanced LLM capabilities to provide more natural and adaptive interactions. The research addresses two key questions: (1) What are the design principles and initial prototype characteristics of an effective LLM-based social robot for autistic students? (2) What improvements can be made based on developer reflection-on-action and expert interviews? The study employed a mixed-methods approach, combining prototype development with qualitative analysis of developer reflections and expert interviews. Key design principles identified include customizability, ethical considerations, and age-appropriate interactions. The initial prototype, built on a Raspberry Pi platform, features custom speech components and basic motor functions. Evaluation of the prototype revealed potential improvements in areas such as user interface, educational value, and practical implementation in educational settings. This research contributes to the growing field of AI-assisted special education by demonstrating the potential of LLM-based social robots in supporting autistic students. The findings provide valuable insights for future developments in accessible and effective social support tools for special education.

Problem

• Develop LLM-based social robot for autistic students
• Enhance social and communication skills naturally
• Improve design through reflections and expert insights

Innovation

• LLM-based social robot
• customizable interaction design
• Raspberry Pi platform